Current manufacturing is dominated by high-mix, low-volume products. In addition, new manufacturing approaches such as 3D printing that are now possible for a large variety of materials, including metals) are revolutionizing the manufacturing process, giving designers unprecedented freedom in designing 3D forms for objects. As a result, surface finishing of manufactured work-pieces such as polishing, grinding and so on is becoming a bottleneck in the manufacturing chain. On the one hand, highly skilled workers might need to be employed to carry out finishing operations of complex surfaces (FIG. 1), making it a lengthy, expensive and operator-dependent process. On the other hand, even when robotic automation is possible, programming robots is still highly time consuming, especially in the case of high-mix, low-volume products, strongly reducing the benefits of automated finishing.
Ideally, robots are employed for various tasks such as material handling, welding, and spray painting that necessitate null or weak interaction between the manipulator and its environment. Conventional approach for operating such industrial robots is through position control. However, a majority of industrial tasks such as surface finishing involve strong physical interaction with the environment and cannot simply depend on position information for task execution. Hence, expert operators are typically required to perform such tasks in spite of high labour cost, health concerns, and quality-control issues.
Automating labour intensive surface finishing tasks require a robot to optimally adapt to unstable interactions with its dynamic workspace. These interactions generate contact forces that should be efficiently measured and controlled in order to achieve the desired end results. Skilled operators can sense these dynamic interactions with the work-piece in terms of 3D forces/torques, and implement appropriate motion and/or force control. This can be achieved through impedance level adjustment in accomplishing the desired task. Human operators learn these skills through years of experience and training, and easily adapt to uncertainties in the task. For a robot to handle a human-like adaptation of a finishing task typically requires a detailed programming and a repeated long-term testing with a high degree of detail for every single micro-motion/activity. Thus, one of the first steps in transitioning from manual to a robotized surface finishing process is developing better understanding of a human operator's knowledge in terms of the interaction with the work-piece applied forces/torques, as well as the motion. However, it is challenging to identify the motor control mechanisms through which skilled operators dexterously manipulate tools and controls the interaction forces, as, at the highest stage of competence, skills are often unconsciously applied. This problem becomes even more significant when handheld tools are used, as the motion and forces involved are not constrained to any dimensions or axis of rotation.